Integrative Approach for Designing Novel Triazole Derivatives as α-Glucosidase Inhibitors: QSAR, Molecular Docking, ADMET, and Molecular Dynamics Investigations

Abstract In response to the increasing prevalence of diabetes mellitus and the limitations associated with the current treatments, there is a growing need to develop novel medications for this disease. This study is focused on creating new compounds that exhibit a strong inhibition of alpha-glucosidase, which is a pivotal enzyme in diabetes control. A set of 33 triazole derivatives underwent an extensive QSAR analysis, aiming to identify the key factors influencing their inhibitory activity against α-glucosidase. Using the multiple linear regression (MLR) model, seven promising compounds were designed as potential drugs. Molecular docking and dynamics simulations were employed to shed light on the mode of interaction between the ligands and the target, and the stability of the obtained complexes. Furthermore, the pharmacokinetic properties of the designed compounds were assessed to predict their behavior in the human body. The binding free energy was also calculated using MMGBSA method and revealed favorable thermodynamic properties. The results highlighted three novel compounds with high biological activity, strong binding affinity to the target enzyme, and suitability for oral administration. These results offer interesting prospects for the development of effective and well-tolerated medications against diabetes mellitus. Dataset License License under which the dataset is made available (CC0, CC-BY, CC-BY-SA, CC-BY-NC, etc.)


Introduction
Diabetes mellitus (DM) is a chronic metabolic disorder characterized by hyperglycemia, which can be caused by reduced insulin action, inadequate insulin synthesis, or both [1].Hyperglycemia typically aggravates the disease burden of diabetes mellitus by contributing to the development of different macrovascular complications like peripheral and autonomic neuropathy, an increased incidence of atherosclerosis, cerebrovascular diseases, neuropathy, nephropathy, and retinopathy [2].
The main feature of diabetes mellitus (DM) is thought to be accompanied by several symptoms including polyuria, polyphagia, weight loss, and blurred vision.
According to the IDF Diabetes Atlas (International Diabetes Federation), 436 million people worldwide were estimated to have diabetes in 2021; by the end of 2045, that number might potentially increase to 700 million [3,4].
The onset of diabetes mellitus is linked to diverse lifestyle factors, including smoking, excessive alcohol consumption, inadequate physical activity, and comorbid conditions such as dyslipidemia and hypertension.Additionally, genetic predisposition, stress, and obesity contribute to the risk of diabetes [5], while enzymes like alpha-glucosidase and amylase The results of the Y-randomization test showed that none of the random trials could match the original model, as indicated in Table 1.The lesser values for R 2 and Q 2 on each iteration, and their averages (R 2 YS = 0.195 and Q 2 YS = −0.341)suggest that the developed QSAR models are not based on random correlations.
The applicability domain (AD) of the model was determined to define the chemical space of set compounds, and it aids in estimating the uncertainty in the prediction of a particular compound based on how similar it is to the training compounds.In this regard, William's plot was employed, considering predictability using three standard deviations and leverage levels below the critical leverage.Therefore, the prediction of a modeled response using QSAR is applicable only if the compound being predicted falls within the AD of the mode.The evaluation's findings demonstrated that neither the training set nor the test set had response values that were outside of the range of responses.The leverages of all the compounds are less than the leverage threshold value of h* = 0.667 (Figure 1), and their standard deviations are all within the ± x range (x = 3) [14].

Design of Novel Compounds
A prior investigation by Emmanuel Oloruntoba Yeye et al. assessed the ability of a set of 33 synthetic chemicals to inhibit α-glucosidase.According to the findings, compounds 14, 16, 20, 21, 25, 27, 28, and 33 showed action that was comparable to that of the well-known α-glucosidase inhibitor Acarbose.The analysis of these compounds consistently revealed the presence of halogen elements, amino groups, and/or nitro groups.
It was discovered that compounds with nitro, amino, or halogen components showed more inhibitory potential, while triazole derivatives with hydroxy groups exhibited less inhibitory potential.
Using the best-selected model and this information as a foundation, this study attempted to create novel drugs with enhanced α-glucosidase inhibitory action.A higher pIC 50 value against α-glucosidase in comparison to the series' most active compounds was the aim.
We concentrated on adding chemical groups like nitro and halogen components to the molecular structure in order to raise the values of the AATSC8s and VE3_Dzs descriptors, and avoiding the addition of hydroxy groups, which were discovered to have adverse effects on the intended activity.We were also able to lower the values of nHsOH, CIC1, and RotBFrac.As indicated in Table 3, seven interesting novel compounds were created by applying these recommended structural alterations to the triazole derivatives.Comparing these compounds to the most active one in the series (Table 4), each one showed a higher pIC 50 percentage.

VE3_Dzs
Logarithmic coefficient sum of the last eigenvector from Barysz matrix weighted by Sanderson EN [18] nHsOH The count of a specific atom-type Hydrogen (H) E-State associated with hydroxy groups (OH) [19] CIC1 1-ordered complementary information content [20]

Design of Novel Compounds
A prior investigation by Emmanuel Oloruntoba Yeye et al. assessed the ability of a set of 33 synthetic chemicals to inhibit α-glucosidase.According to the findings, compounds 14, 16, 20, 21, 25, 27, 28, and 33 showed action that was comparable to that of the well-known α-glucosidase inhibitor Acarbose.The analysis of these compounds consistently revealed the presence of halogen elements, amino groups, and/or nitro groups.
It was discovered that compounds with nitro, amino, or halogen components showed more inhibitory potential, while triazole derivatives with hydroxy groups exhibited less inhibitory potential.
Using the best-selected model and this information as a foundation, this study attempted to create novel drugs with enhanced α-glucosidase inhibitory action.A higher pIC50 value against α-glucosidase in comparison to the series' most active compounds was the aim.
We concentrated on adding chemical groups like nitro and halogen components to the molecular structure in order to raise the values of the AATSC8s and VE3_Dzs descriptors, and avoiding the addition of hydroxy groups, which were discovered to have adverse effects on the intended activity.We were also able to lower the values of nHsOH, CIC1, and RotBFrac.As indicated in Table 3, seven interesting novel compounds were created by applying these recommended structural alterations to the triazole derivatives.Comparing these compounds to the most active one in the series (Table 4), each one showed a higher pIC50 percentage.

Design of Novel Compounds
A prior investigation by Emmanuel Oloruntoba Yeye et al. assessed the ability of a set of 33 synthetic chemicals to inhibit α-glucosidase.According to the findings, compounds 14, 16, 20, 21, 25, 27, 28, and 33 showed action that was comparable to that of the well-known α-glucosidase inhibitor Acarbose.The analysis of these compounds consistently revealed the presence of halogen elements, amino groups, and/or nitro groups.
It was discovered that compounds with nitro, amino, or halogen components showed more inhibitory potential, while triazole derivatives with hydroxy groups exhibited less inhibitory potential.
Using the best-selected model and this information as a foundation, this study attempted to create novel drugs with enhanced α-glucosidase inhibitory action.A higher pIC50 value against α-glucosidase in comparison to the series' most active compounds was the aim.
We concentrated on adding chemical groups like nitro and halogen components to the molecular structure in order to raise the values of the AATSC8s and VE3_Dzs descriptors, and avoiding the addition of hydroxy groups, which were discovered to have adverse effects on the intended activity.We were also able to lower the values of nHsOH, CIC1, and RotBFrac.As indicated in Table 3, seven interesting novel compounds were created by applying these recommended structural alterations to the triazole derivatives.Comparing these compounds to the most active one in the series (Table 4), each one showed a higher pIC50 percentage.

Design of Novel Compounds
A prior investigation by Emmanuel Oloruntoba Yeye et al. assessed the ability of a set of 33 synthetic chemicals to inhibit α-glucosidase.According to the findings, compounds 14, 16, 20, 21, 25, 27, 28, and 33 showed action that was comparable to that of the well-known α-glucosidase inhibitor Acarbose.The analysis of these compounds consistently revealed the presence of halogen elements, amino groups, and/or nitro groups.
It was discovered that compounds with nitro, amino, or halogen components showed more inhibitory potential, while triazole derivatives with hydroxy groups exhibited less inhibitory potential.
Using the best-selected model and this information as a foundation, this study attempted to create novel drugs with enhanced α-glucosidase inhibitory action.A higher pIC50 value against α-glucosidase in comparison to the series' most active compounds was the aim.
We concentrated on adding chemical groups like nitro and halogen components to the molecular structure in order to raise the values of the AATSC8s and VE3_Dzs descriptors, and avoiding the addition of hydroxy groups, which were discovered to have adverse effects on the intended activity.We were also able to lower the values of nHsOH, CIC1, and RotBFrac.As indicated in Table 3, seven interesting novel compounds were created by applying these recommended structural alterations to the triazole derivatives.Comparing these compounds to the most active one in the series (Table 4), each one showed a higher pIC50 percentage.

Design of Novel Compounds
A prior investigation by Emmanuel Oloruntoba Yeye et al. assessed the ability of a set of 33 synthetic chemicals to inhibit α-glucosidase.According to the findings, compounds 14, 16, 20, 21, 25, 27, 28, and 33 showed action that was comparable to that of the well-known α-glucosidase inhibitor Acarbose.The analysis of these compounds consistently revealed the presence of halogen elements, amino groups, and/or nitro groups.
It was discovered that compounds with nitro, amino, or halogen components showed more inhibitory potential, while triazole derivatives with hydroxy groups exhibited less inhibitory potential.
Using the best-selected model and this information as a foundation, this study attempted to create novel drugs with enhanced α-glucosidase inhibitory action.A higher pIC50 value against α-glucosidase in comparison to the series' most active compounds was the aim.
We concentrated on adding chemical groups like nitro and halogen components to the molecular structure in order to raise the values of the AATSC8s and VE3_Dzs descriptors, and avoiding the addition of hydroxy groups, which were discovered to have adverse effects on the intended activity.We were also able to lower the values of nHsOH, CIC1, and RotBFrac.As indicated in Table 3, seven interesting novel compounds were created by applying these recommended structural alterations to the triazole derivatives.Comparing these compounds to the most active one in the series (Table 4), each one showed a higher pIC50 percentage.

Design of Novel Compounds
A prior investigation by Emmanuel Oloruntoba Yeye et al. assessed the ability of a set of 33 synthetic chemicals to inhibit α-glucosidase.According to the findings, compounds 14, 16, 20, 21, 25, 27, 28, and 33 showed action that was comparable to that of the well-known α-glucosidase inhibitor Acarbose.The analysis of these compounds consistently revealed the presence of halogen elements, amino groups, and/or nitro groups.
It was discovered that compounds with nitro, amino, or halogen components showed more inhibitory potential, while triazole derivatives with hydroxy groups exhibited less inhibitory potential.
Using the best-selected model and this information as a foundation, this study attempted to create novel drugs with enhanced α-glucosidase inhibitory action.A higher pIC50 value against α-glucosidase in comparison to the series' most active compounds was the aim.
We concentrated on adding chemical groups like nitro and halogen components to the molecular structure in order to raise the values of the AATSC8s and VE3_Dzs descriptors, and avoiding the addition of hydroxy groups, which were discovered to have adverse effects on the intended activity.We were also able to lower the values of nHsOH, CIC1, and RotBFrac.As indicated in Table 3, seven interesting novel compounds were created by applying these recommended structural alterations to the triazole derivatives.Comparing these compounds to the most active one in the series (Table 4), each one showed a higher pIC50 percentage.

Design of Novel Compounds
A prior investigation by Emmanuel Oloruntoba Yeye et al. assessed the ability of a set of 33 synthetic chemicals to inhibit α-glucosidase.According to the findings, compounds 14, 16, 20, 21, 25, 27, 28, and 33 showed action that was comparable to that of the well-known α-glucosidase inhibitor Acarbose.The analysis of these compounds consistently revealed the presence of halogen elements, amino groups, and/or nitro groups.
It was discovered that compounds with nitro, amino, or halogen components showed more inhibitory potential, while triazole derivatives with hydroxy groups exhibited less inhibitory potential.
Using the best-selected model and this information as a foundation, this study attempted to create novel drugs with enhanced α-glucosidase inhibitory action.A higher pIC50 value against α-glucosidase in comparison to the series' most active compounds was the aim.
We concentrated on adding chemical groups like nitro and halogen components to the molecular structure in order to raise the values of the AATSC8s and VE3_Dzs descriptors, and avoiding the addition of hydroxy groups, which were discovered to have adverse effects on the intended activity.We were also able to lower the values of nHsOH, CIC1, and RotBFrac.As indicated in Table 3, seven interesting novel compounds were created by applying these recommended structural alterations to the triazole derivatives.Comparing these compounds to the most active one in the series (Table 4), each one showed a higher pIC50 percentage.

Design of Novel Compounds
A prior investigation by Emmanuel Oloruntoba Yeye et al. assessed the ability of a set of 33 synthetic chemicals to inhibit α-glucosidase.According to the findings, compounds 14, 16, 20, 21, 25, 27, 28, and 33 showed action that was comparable to that of the well-known α-glucosidase inhibitor Acarbose.The analysis of these compounds consistently revealed the presence of halogen elements, amino groups, and/or nitro groups.
It was discovered that compounds with nitro, amino, or halogen components showed more inhibitory potential, while triazole derivatives with hydroxy groups exhibited less inhibitory potential.
Using the best-selected model and this information as a foundation, this study attempted to create novel drugs with enhanced α-glucosidase inhibitory action.A higher pIC50 value against α-glucosidase in comparison to the series' most active compounds was the aim.
We concentrated on adding chemical groups like nitro and halogen components to the molecular structure in order to raise the values of the AATSC8s and VE3_Dzs descriptors, and avoiding the addition of hydroxy groups, which were discovered to have adverse effects on the intended activity.We were also able to lower the values of nHsOH, CIC1, and RotBFrac.As indicated in Table 3, seven interesting novel compounds were created by applying these recommended structural alterations to the triazole derivatives.Comparing these compounds to the most active one in the series (Table 4), each one showed a higher pIC50 percentage.

Applicability Domain
The obtained leverage values for the designed molecules were compared with the warning leverage (h*).The compound was recommended to be inside the applicability domain of the model based on the leverage (hi) being smaller than the warning leverage (h*).In this case, the superscript t denotes the transpose of the matrix or vector of the designed molecules, n is the number of training set compounds, and k is the number of model descriptors.xi is the matrix of model descriptors of each designed molecule, and X is the matrix of model descriptor values for n training set compounds [14].Using the leverage threshold h* as a guide, we computed the leverages of every molecule proposed.Based on the values of hi, which has the range of (0.055-0.358), the findings displayed in Table 4 indicate that all of these compounds are acceptable.

Molecular Docking
The molecular docking of all investigated compounds was conducted within the active site of the target receptor.The findings are illustrated in Figures 2 and 3 and Table 5 and demonstrate favorable binding affinities for all complexes, attributed to the diverse interactions established between the ligands and key residues situated in the binding site.Acarbose, recognized as an alpha-glucosidase inhibitor, was included in the docking simulations to elucidate the interactions formed within the receptor.The Acarbose-2f6d complex revealed notable interactions, including five Conventional Hydrogen Bonds with Arg69, Glu211, Glu210, Leu208, and Asp70 residues, as well as two Carbon-Hydrogen Bonds with Trp209 and Ala138, and a Pi-Sigma interaction with Tyr351.Distances for these interactions ranged between 1.84 Å and 3.79 Å.Moreover, Water Hydrogen bonds were detected, underscoring the involvement of Water molecules in the formation of this intricate complex.HOH1672, HOH1504, HOH1672, and HOH1723 at distances ranging between 2.92 and 5.29 Å.For Compound P4, a complex was formed through a Carbon-Hydrogen Bond, Pi-Anion, and Pi-Pi Stacked interactions with Trp209, Glu210, and Trp139 residues.Water Hydrogen Bonds with HOH1433 were also detected.
In the case of Compound P10, a complex was formed through a Conventional Hydrogen Bond, Carbon-Hydrogen Bond, and Pi-Pi Stacked interactions with Arg69, Trp209, Glu211, Glu210, and Tyr351.Water Hydrogen Bonds were observed with HOH1464, HOH1672, HOH1723, HOH1189, and HOH1282.

P19-2f6d
Acarbose-2f6d Compound P14 exhibited a diverse array of interactions, including a Conventional Hydrogen Bond, Carbon-Hydrogen Bond, Pi-Cation, Pi-Sigma, and Pi-Pi Stacked interactions with Arg345, Glu210, Trp209, Tyr351, and Trp139 residues, while the complex formed by Compound 19 involved Pi-Cation and Pi-Pi Stacked interactions with Arg69 and Tyr351, along with Water Hydrogen Bonds with HOH1723 and HOH1464.
Among the compounds subjected to docking, ligands P6, P10, and P14 were anticipated to establish interactions similar to Acarbose, including Conventional Hydrogen bonds, Carbon-Hydrogen bonds, and Pi-Sigma interactions.These interactions were projected to involve the same residues, such as Arg69, Trp209, Glu210, Glu211, and Tyr351.This anticipation suggests a potential similarity in the binding mechanism between these ligands and the reference drug, indicating a likelihood of shared effects within the studied receptor.This observation implies that these ligands may operate through a comparable mechanism to Acarbose, demonstrating a potential therapeutic impact on the target receptor.These interactions observed, including Conventional Hydrogen Bonds, Carbon-Hydrogen Bonds, and Pi-Sigma interaction with key residues and additional interactions with Water molecules, are indicative of a complex and intricate binding pattern within the active site of the receptor.Such interactions are crucial and may play a pivotal role in inhibiting the activity of the alpha-glucosidase enzyme.The binding affinity and specificity of the ligands towards the receptor, as evidenced by these interactions, suggest a potential mechanism for effective inhibition, contributing to the therapeutic impact of the studied compounds on alpha-glucosidase activity.

ADMET Properties Prediction
Compound P3 engaged in intricate interactions within the receptor's active site, including two Pi-Pi Stacked and two Pi-Alkyl interactions with Trp139 and Tyr351 residues.Additionally, Water Hydrogen Bonds were observed with HOH1163, HOH1189, HOH1672, HOH1504, HOH1672, and HOH1723 at distances ranging between 2.92 and 5.29 Å.
For Compound P4, a complex was formed through a Carbon-Hydrogen Bond, Pi-Anion, and Pi-Pi Stacked interactions with Trp209, Glu210, and Trp139 residues.Water Hydrogen Bonds with HOH1433 were also detected.
In the case of Compound P10, a complex was formed through a Conventional Hydrogen Bond, Carbon-Hydrogen Bond, and Pi-Pi Stacked interactions with Arg69, Trp209, Glu211, Glu210, and Tyr351.Water Hydrogen Bonds were observed with HOH1464, HOH1672, HOH1723, HOH1189, and HOH1282.
Compound P14 exhibited a diverse array of interactions, including a Conventional Hydrogen Bond, Carbon-Hydrogen Bond, Pi-Cation, Pi-Sigma, and Pi-Pi Stacked interactions with Arg345, Glu210, Trp209, Tyr351, and Trp139 residues, while the complex formed by Compound 19 involved Pi-Cation and Pi-Pi Stacked interactions with Arg69 and Tyr351, along with Water Hydrogen Bonds with HOH1723 and HOH1464.
Among the compounds subjected to docking, ligands P6, P10, and P14 were anticipated to establish interactions similar to Acarbose, including Conventional Hydrogen bonds, Carbon-Hydrogen bonds, and Pi-Sigma interactions.These interactions were projected to involve the same residues, such as Arg69, Trp209, Glu210, Glu211, and Tyr351.This anticipation suggests a potential similarity in the binding mechanism between these ligands and the reference drug, indicating a likelihood of shared effects within the studied receptor.This observation implies that these ligands may operate through a comparable mechanism to Acarbose, demonstrating a potential therapeutic impact on the target receptor.

ADMET Properties Prediction
The pharmacokinetic properties of the studied compounds were predicted using the pkCSM online tools.The results of the analysis are listed in Table 6.It was found that all investigated compounds adhere to the Lipinski rules.
All of the substances are expected to have high absorption from the gastrointestinal (GI) tract into the bloodstream, indicating their potency for absorption, based on the information provided in Table 6.Additionally, the compounds showed excellent Water solubility, which is advantageous for absorption.All compounds except for Compounds P10 and P19 exhibited strong Caco-2 permeability.
Since none of the substances were expected to be P-glycoprotein inhibitors, it is unlikely that they will obstruct the efflux transporters that allow medications to be pumped out of cells.Compounds P7 and P14, on the other hand, were predicted to be P-glycoprotein substrates, which means P-glycoprotein could be able to detect and transport them.Furthermore, it was noted that all compounds had a high level of skin permeability, signifying its capacity to penetrate the epidermal barrier and enter the body.Here, a LogKp value of less than −2.5 is considered high-skin-permeability.
All compounds were predicted to have a low volume of distribution at steady state (VDss) values, except Compounds P3 and P7.In this context, a low VDss value is defined as a LogVDss value less than −0.15, indicating that in a steady state, the chemicals have a relatively small volume of distribution throughout the body.Compound P14 was predicted to have a modest potential to cross the blood-brain barrier, as demonstrated by a LogBB (logarithm of the blood-brain barrier partition coefficient) higher than 0.3.The remaining compounds were estimated to have a modest potential to cross.Compound 3 was found to have the potency to penetrate the central nervous system (CNS), while Compounds P6, P7, and P10 were considered unable to penetrate the CNS.
There was a low chance that any of the substances would interact with any of these specific cytochrome P450 enzymes because it was anticipated that they would all be inhibitors of CYP1A2 and neither substrates nor inhibitors of CYP2D6, CYP2C19, CYP2C9, or CYP3A4.Nevertheless, it was considered that Compounds P6 and P14 were CYP3A4 substrates, indicating that the CYP3A4 enzyme could metabolize them.
Except for Compound P7, which was thought to be an OCT2 substrate and may be carried via the kidney's OCT2 transporter, other compounds were predicted to be non-renal OCT2 substrates.The range of the medications' total clearance values (Log(ml/min/kg)) indicates potential dose rates required to achieve steady-state concentrations, from 0.063 to 0.586.
Since the compounds were projected to be neither hERG I nor hERG II inhibitors, it is unlikely that they will have a major impact on the hERG channel, which is crucial for heart function.Additionally, it was anticipated that they would not produce skin sensitization, which means that when they come into contact with skin, they will likely not trigger allergic responses.
According to the AMES test, chemicals P6, P10, and P14 were expected to not introduce AMES toxicity, indicating that it is unlikely that they will result in bacterial cell mutations.

Molecular Dynamics Simulation
Ligands P6, P10, and P14 were selected for molecular dynamics simulation based on their favorable pharmacological properties, good binding scores, and interactions with key residues of the analyzed enzyme.

Root-Mean-Squared Deviation
A 100 ns simulation was conducted on the protein-ligand complexes (2f6d with P6, P10, and P14) and the uncomplexed protein to observe any deviations or structural changes induced when the protein was bound to the selected ligands.The Root-Mean-Square Deviation (RMSD) values for the proteins were computed and are visualized in Figure 4.The outcomes revealed a consistent state with an RMSD consistently below 3 Å over the entire simulation period.This signifies that the complexes attained a stable conformation, indicating stability despite their interactions with the examined ligands.The persistent and low RMSD values suggest that the complexes maintained structural integrity throughout the simulation.Figure 5 illustrates the RMSD values of the proposed ligands during their interaction with the target.The findings suggest stability throughout the simulation for P6 and P14, with average RMSD values of 5.52 Å and 2.82 Å, respectively.In contrast, P10 initially maintained a consistent state for the first thirty nanoseconds before exhibiting fluctuations exceeding 15 Å, and then a decrease in RMSD value was eventually noted towards the end of the simulation.The average RMSD of P10 was 8.16 Å.

Root-Mean-Squared Fluctuation
The investigation included a Root-Mean-Squared Fluctuation (RMSF) analysis for all simulated complexes, and a separate simulation was conducted for the uncomplexed protein to compare residue fluctuations during the simulation period.The primary objective of this analysis was to evaluate the stability of protein residues in the presence of the investigated ligands.The RMSF values for protein residues remained below 3 Å for all Figure 5 illustrates the RMSD values of the proposed ligands during their interaction with the target.The findings suggest stability throughout the simulation for P6 and P14, with average RMSD values of 5.52 Å and 2.82 Å, respectively.In contrast, P10 initially maintained a consistent state for the first thirty nanoseconds before exhibiting fluctuations exceeding 15 Å, and then a decrease in RMSD value was eventually noted towards the end of the simulation.The average RMSD of P10 was 8.16 Å.  Figure 5 illustrates the RMSD values of the proposed ligands during their interaction with the target.The findings suggest stability throughout the simulation for P6 and P14, with average RMSD values of 5.52 Å and 2.82 Å, respectively.In contrast, P10 initially maintained a consistent state for the first thirty nanoseconds before exhibiting fluctuations exceeding 15 Å, and then a decrease in RMSD value was eventually noted towards the end of the simulation.The average RMSD of P10 was 8.16 Å.

Root-Mean-Squared Fluctuation
The investigation included a Root-Mean-Squared Fluctuation (RMSF) analysis for all simulated complexes, and a separate simulation was conducted for the uncomplexed protein to compare residue fluctuations during the simulation period.The primary objective of this analysis was to evaluate the stability of protein residues in the presence of the investigated ligands.The RMSF values for protein residues remained below 3 Å for all  The investigation included a Root-Mean-Squared Fluctuation (RMSF) analysis for all simulated complexes, and a separate simulation was conducted for the uncomplexed protein to compare residue fluctuations during the simulation period.The primary objective of this analysis was to evaluate the stability of protein residues in the presence of the investigated ligands.The RMSF values for protein residues remained below 3 Å for all simulated complexes (Figure 6), indicating the absence of significant fluctuations.This implies that a state of stability was achieved for the protein residues despite their interactions with the studied ligands.
Pharmaceuticals 2024, 17, x FOR PEER REVIEW 16 of 26 simulated complexes (Figure 6), indicating the absence of significant fluctuations.This implies that a state of stability was achieved for the protein residues despite their interactions with the studied ligands.

Protein-Ligand Contact
The ligand-protein interactions were thoroughly investigated, uncovering a diverse range of molecular bonds and bridges contributing to their binding affinity, as illustrated in Table 7. Specifically, P6 formed Hydrogen bonds with Tyr63, indicating a directed interaction.Hydrophobic interactions played a crucial role, with P6 engaging hydrophobic residues Trp209, Tyr351, Trp362, and Trp473, enhancing complex stability.Ionic bonds with Trp67, Asp70, and Glu210 added an electrostatic dimension to the interaction.Water molecules acted as bridges, mediating specific interactions with Tyr63, Arg69, Asp70, Trp209, and Glu210.The persistence of interactions, particularly with Asp63, Asp70, Trp209, Glu210, Tyr351, and Trp362 residues, underscored their crucial role in maintaining complex stability.

Protein-Ligand Contact
The ligand-protein interactions were thoroughly investigated, uncovering a diverse range of molecular bonds and bridges contributing to their binding affinity, as illustrated in Table 7. Specifically, P6 formed Hydrogen bonds with Tyr63, indicating a directed interaction.Hydrophobic interactions played a crucial role, with P6 engaging hydrophobic residues Trp209, Tyr351, Trp362, and Trp473, enhancing complex stability.Ionic bonds with Trp67, Asp70, and Glu210 added an electrostatic dimension to the interaction.Water molecules acted as bridges, mediating specific interactions with Tyr63, Arg69, Asp70, Trp209, and Glu210.The persistence of interactions, particularly with Asp63, Asp70, Trp209, Glu210, Tyr351, and Trp362 residues, underscored their crucial role in maintaining complex stability.
Table 7. Protein-ligand contact (histogram and timeline) of all simulated complexes after 100 ns (Pink: ionic bond; bleu: water bridge; violet: hydrophobic bond, and Green: Hydrogen bond).
Table 7. Protein-ligand contact (histogram and timeline) of all simulated complexes after 100 ns (Pink: ionic bond; bleu: water bridge; violet: hydrophobic bond, and Green: Hydrogen bond).
Table 7. Protein-ligand contact (histogram and timeline) of all simulated complexes after 100 ns (Pink: ionic bond; bleu: water bridge; violet: hydrophobic bond, and Green: Hydrogen bond).
Table 7. Protein-ligand contact (histogram and timeline) of all simulated complexes after 100 ns (Pink: ionic bond; bleu: water bridge; violet: hydrophobic bond, and Green: Hydrogen bond).
Table 7. Protein-ligand contact (histogram and timeline) of all simulated complexes after 100 ns (Pink: ionic bond; bleu: water bridge; violet: hydrophobic bond, and Green: Hydrogen bond).
Table 7. Protein-ligand contact (histogram and timeline) of all simulated complexes after 100 ns (Pink: ionic bond; bleu: water bridge; violet: hydrophobic bond, and Green: Hydrogen bond).
Table 7. Protein-ligand contact (histogram and timeline) of all simulated complexes after 100 ns (Pink: ionic bond; bleu: water bridge; violet: hydrophobic bond, and Green: Hydrogen bond).

Binding Free Energy
The binding free energy calculations for the three simulated complexes, P6-2f6d, P10-2f6d, and P14-2f6d, revealed favorable thermodynamic stability.The negative values of ∆G (−32.59kcal/mol, −35.8 kcal/mol, and −41.17 kcal/mol, respectively) indicate spontaneous and energetically favorable interactions between the ligands and the protein.

QSAR Model Construction and Validation
The QSARINS software was utilized to build the QSAR models.This program is well known for producing statistically strong GA-MLR-based QSAR models.QSARINS's random splitting option was used to divide the dataset into training and test sets at random for this purpose [27].Twenty percent of the compounds were in the test set and eighty percent were in the training set [28].The models were generated using a representative subset of the dataset and assessed on a different collection of substances thanks to this tried-and-true methodology [29].
By including the maximum number of molecular descriptors impacting or altering biological potential/activity, this method aimed to enhance the performance of the models [30].The calculated molecular descriptors are used as independent variables to predict or explain the activity of a molecule by employing Formula (1): In accordance with the OECD guidelines [31,32], the obtained models underwent comprehensive external and internal statistical validations, Y-randomization, and applicability domain analysis.A variety of model evaluation metrics and statistical parameters were taken into account to assess the models' performance and choose the best model.These included the coefficient adjusted for degrees of freedom (R 2 adj ), coefficient of determination for the test set (R 2 test ), root-mean-squared error (RMSE), and R-squared coefficient of determination (R 2 ).As the fitness function, the leave-one-out cross-validation coefficient, or Q 2  LOO , was also given.Typically, measurements with values higher than 0.6 signify a more stable and consistent model [33][34][35].
Leverage analysis-expressed as William's plot-is used in chemometrics and QSAR analysis to assess a model's applicability domain (AD) by determining the standardized residuals (r) and the leverage threshold values (h* = (3 × (k + 1))/n), where n denotes the number of trainings and k the number of descriptors.The range that the model is thought to be dependable for forecasting new values is represented by the AD [36,37].

Molecular Docking
The molecular docking method is widely used to predict the best poses of the examined ligands when docked in the active pocket of the target, as well as the binding affinity values and the created molecular interactions between the residues located in the receptor and the studied ligands [38].
The receptor utilized in this investigation was sourced from the Protein Data Bank, identified by the PDB ID of 2f6d [39], corresponding to the complex structure involving a glucoamylase from Saccharomycopsis fibuligera and Acarbose.
The selection of this particular structure was influenced by its intricate nature, resembling that of Acarbose.Acarbose is renowned for its effectiveness in inhibiting the alpha-glucosidase enzyme.Furthermore, the high resolution of this structure, standing at 1.60 Å (below 2 Å), adds to its suitability.The receptor comprises 492 amino acids in the 'Chain A', referred to as glucoamylase (Figure 7A).It also contains additional heteroatoms such as alpha-acarbose, phosphate, and sodium ions, which were excluded before the commencement of the molecular docking phase [39].
In preparation for the docking simulations, the receptor underwent necessary adjustments, including minimizing the energy of the protein structure using Swiss PDB Viewer [40], the addition of polar Hydrogens, and computation of Gasteiger charges using AutoDock vina software [41][42][43].In this case, given the presence of Water molecules within the binding site of the protein, they were preserved to explore potential interactions with the docked ligands.The ligands underwent an energy minimization process employing the MMFF94 force field through Avogadro software [44].This step was undertaken to refine and optimize the structural conformation of the ligands.In preparation for the docking simulations, the receptor underwent necessary adjustments, including minimizing the energy of the protein structure using Swiss PDB Viewer [40], the addition of polar Hydrogens, and computation of Gasteiger charges using Auto-Dock vina software [41][42][43].In this case, given the presence of Water molecules within the binding site of the protein, they were preserved to explore potential interactions with the docked ligands.The ligands underwent an energy minimization process employing the MMFF94 force field through Avogadro software [44].This step was undertaken to refine and optimize the structural conformation of the ligands.
The choice of the grid box for docking was strategic, determined by the initial position of the co-crystalized ligand (Acarbose), which is acknowledged as an inhibitor of alpha-glucosidase activity [45], serving as the reference drug for comparative analysis of interactions.The coordinates of the binding site were meticulously set at x = 12.68 Å, y = 10.80 Å, z = −6.35Å, with a size of 20 Å 3 and a space center of 0.375 Å (Figure 7B).
The molecular docking process was iterated five times to ensure robustness and reliability.The conformations of the ligands were selected based on their frequency of appearance across these multiple runs.Notably, all chosen conformations exhibited consistent presence in every run, affirming their reliability and reinforcing the confidence in their representativeness.Figure 8 provides a visualization of the frequency for the obtained conformations in each run.The choice of the grid box for docking was strategic, determined by the initial position of the co-crystalized ligand (Acarbose), which is acknowledged as an inhibitor of alpha-glucosidase activity [45], serving as the reference drug for comparative analysis of interactions.The coordinates of the binding site were meticulously set at x = 12.68 Å, y = 10.80 Å, z = −6.35Å, with a size of 20 Å 3 and a space center of 0.375 Å (Figure 7B).
The molecular docking process was iterated five times to ensure robustness and reliability.The conformations of the ligands were selected based on their frequency of appearance across these multiple runs.Notably, all chosen conformations exhibited consistent presence in every run, affirming their reliability and reinforcing the confidence in their representativeness.Figure 8 provides a visualization of the frequency for the obtained conformations in each run.In preparation for the docking simulations, the receptor underwent necessary adjustments, including minimizing the energy of the protein structure using Swiss PDB Viewer [40], the addition of polar Hydrogens, and computation of Gasteiger charges using Auto-Dock vina software [41][42][43].In this case, given the presence of Water molecules within the binding site of the protein, they were preserved to explore potential interactions with the docked ligands.The ligands underwent an energy minimization process employing the MMFF94 force field through Avogadro software [44].This step was undertaken to refine and optimize the structural conformation of the ligands.
The choice of the grid box for docking was strategic, determined by the initial position of the co-crystalized ligand (Acarbose), which is acknowledged as an inhibitor of alpha-glucosidase activity [45], serving as the reference drug for comparative analysis of interactions.The coordinates of the binding site were meticulously set at x = 12.68 Å, y = 10.80 Å, z = −6.35Å, with a size of 20 Å 3 and a space center of 0.375 Å (Figure 7B).
The molecular docking process was iterated five times to ensure robustness and reliability.The conformations of the ligands were selected based on their frequency of appearance across these multiple runs.Notably, all chosen conformations exhibited consistent presence in every run, affirming their reliability and reinforcing the confidence in their representativeness.Figure 8 provides a visualization of the frequency for the obtained conformations in each run.The co-crystalized ligand (Acarbose) was initially docked into the binding site of the receptor to validate the docking protocol [8].Subsequently, AutoDock tools [46] facilitated the molecular docking of all designed compounds, enabling an in-depth exploration of potential interactions and the determination of binding affinities within the active site of the receptor.The docking simulation was conducted through a total of 9 runs, and the complex resulting from the run with the lowest binding affinity, corresponding to an RMSD of 0, was selected and subjected to analysis [47].The calculated Root-Mean-Squared Deviation (RMSD) value of 0.217 Å (below 2 Å) indicates a minimal deviation between the initial ligand and the docked ligand (Figure 9).This result affirms the precision of the docking protocol in faithfully reproducing the binding pose of the reference ligand within the active site of the receptor [48].
complex resulting from the run with the lowest binding affinity, corresponding to an RMSD of 0, was selected and subjected to analysis [47].The calculated Root-Mean-Squared Deviation (RMSD) value of 0.217 Å (below 2 Å) indicates a minimal deviation between the initial ligand and the docked ligand (Figure 9).This result affirms the precision of the docking protocol in faithfully reproducing the binding pose of the reference ligand within the active site of the receptor [48].

ADMET Analysis
The designed compounds with a high value of pIC50 against alpha-glucosidase activity were subjected to an ADMET analysis for the purpose of gaining insight into the pharmacological properties which includes several properties such as the absorption, distribution, metabolism, excretion, and toxicity [49,50].The pkCSM website is widely used for predicting these properties and evaluating the behavior of compounds in the human body.
Lipinski's rules were used to eliminate the compounds that do not respect the threshold values of these principles, including a molecular weight of less than 500 g/Mol, no more than 5 donor bonds, no more than 10 acceptor bonds, and a partition coefficient (LogP) no more than 5 [51].Additionally, several properties were evaluated to determine the behavior of the analyzed compounds in the human body [51] such as Caco-2 permeability, Intestinal absorption (human), and the volume of distribution at steady state (VDss).These evaluations offer interesting details into the potential behavior and suitability of the compounds for further development.

Molecular Dynamics (MD) Simulation
To gain insight into the structural changes that befell the protein and ligand through key parameters like the (RMSD) Root-Mean-Squared Deviation and (RMSF) Root-Mean-Squared Fluctuation, the hit compounds with the best binding score, highest biological activity, and good pharmacological properties were put through a molecular dynamics' simulation.The generated molecular interactions between them were then assessed to determine the cause of stability or changes observed in the protein and ligand structures [52,53].
The generated complexes were prepared, minimized, and optimized under the OPLS3e force field [54] using the protein preparation wizard which is available in Maestro software [55].More recently, we used the Water model (TIP3P) to build an orthorhombic simulation [56].Following the addition of Na + and Cl − counterions to neutralize the charge of the solvated systems, the physiological salt concentration was adjusted to 0.15 M.After that, the system was heated gradually to the target temperature (which is under 300 K and

ADMET Analysis
The designed compounds with a high value of pIC 50 against alpha-glucosidase activity were subjected to an ADMET analysis for the purpose of gaining insight into the pharmacological properties which includes several properties such as the absorption, distribution, metabolism, excretion, and toxicity [49,50].The pkCSM website is widely used for predicting these properties and evaluating the behavior of compounds in the human body.
Lipinski's rules were used to eliminate the compounds that do not respect the threshold values of these principles, including a molecular weight of less than 500 g/Mol, no more than 5 donor bonds, no more than 10 acceptor bonds, and a partition coefficient (LogP) no more than 5 [51].Additionally, several properties were evaluated to determine the behavior of the analyzed compounds in the human body [51] such as Caco-2 permeability, Intestinal absorption (human), and the volume of distribution at steady state (VDss).These evaluations offer interesting details into the potential behavior and suitability of the compounds for further development.

Molecular Dynamics (MD) Simulation
To gain insight into the structural changes that befell the protein and ligand through key parameters like the (RMSD) Root-Mean-Squared Deviation and (RMSF) Root-Mean-Squared Fluctuation, the hit compounds with the best binding score, highest biological activity, and good pharmacological properties were put through a molecular dynamics' simulation.The generated molecular interactions between them were then assessed to determine the cause of stability or changes observed in the protein and ligand structures [52,53].
The generated complexes were prepared, minimized, and optimized under the OPLS3e force field [54] using the protein preparation wizard which is available in Maestro software [55].More recently, we used the Water model (TIP3P) to build an orthorhombic simulation [56].Following the addition of Na + and Cl − counterions to neutralize the charge of the solvated systems, the physiological salt concentration was adjusted to 0.15 M.After that, the system was heated gradually to the target temperature (which is under 300 K and one bar of pressure) using the Nose-Hoover thermal algorithm and the Martina-Tobias-Klein method [57].A recording interval, an isothermal-isobaric ensemble (NPT), and an MD simulation duration of 100 ns were all employed.In this work, MD simulations were performed using the Desmond package [58], which is part of the Schrödinger 2020-3 academic program.
The Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) technique, a crucial component of the Maestro software package, was used to calculate the binding free energy for the simulated complexes.The binding free energies in molecular dynamics simulations were estimated using MM/GBSA.It is especially helpful for comprehending the thermodynamics of ligand binding in molecular dynamics simulations and offers a thorough and effective method of analyzing the energetics of protein-ligand interactions [59].

Conclusions
This study employed a comprehensive QSAR analysis of 33 triazole derivatives to identify the key factors influencing their inhibitory activity against alpha-glucosidase.This analysis aimed to identify novel potential compounds that could serve as novel medications for diabetes mellitus.Based on the best selected MLR model, seven promising compounds were designed as potential drugs, and subjected to the molecular docking and dynamics simulations to gain insights into the stability and the mode of interaction between the designed compounds and the target enzyme.The pharmacokinetic properties of the compounds were assessed to predict their behavior in the human body through various parameters such as absorption, distribution, metabolism, and excretion of the compounds.The results highlighted three novel compounds (P6, P10, and P14) with high biological activity, strong binding affinity to the target enzyme, favorable thermodynamic properties, and suitability for oral administration.

Figure 1 .
Figure 1.William's plot of the developed model.

Figure 1 .
Figure 1.William's plot of the developed model.

Figure 2 .
Figure 2. Three-dimensional visualization of the docked ligands into the binding site of alphaglucosidase receptor.Compound P3 engaged in intricate interactions within the receptor's active site, including two Pi-Pi Stacked and two Pi-Alkyl interactions with Trp139 and Tyr351 residues.Additionally, Water Hydrogen Bonds were observed with HOH1163, HOH1189,

Figure 2 .
Figure 2. Three-dimensional visualization of the docked ligands into the binding site of alphaglucosidase receptor.

Figure 3 .
Figure 3.The 2D visualization of created complexes after molecular docking, with distances, interaction types, and participating residues.

Figure 3 .
Figure 3.The 2D visualization of created complexes after molecular docking, with distances, interaction types, and participating residues.

Figure 4 .
Figure 4. Protein RMSD plot of all simulated complexes after 100 ns.

Figure 5 .
Figure 5. Ligand RMSD plot of all simulated complexes after 100 ns.

Figure 4 .
Figure 4. Protein RMSD plot of all simulated complexes after 100 ns.

Figure 4 .
Figure 4. Protein RMSD plot of all simulated complexes after 100 ns.

Figure 5 .
Figure 5. Ligand RMSD plot of all simulated complexes after 100 ns.

Figure 5 .
Figure 5. Ligand RMSD plot of all simulated complexes after 100 ns.

Figure 7 .
Figure 7. (A) The 3D visualization of the 2f6d receptor; (B) the binding site of the 2f6d receptor.

Figure 7 .
Figure 7. (A) The 3D visualization of the 2f6d receptor; (B) the binding site of the 2f6d receptor.

Figure 7 .
Figure 7. (A) The 3D visualization of the 2f6d receptor; (B) the binding site of the 2f6d receptor.

Figure 8 .
Figure 8. Frequency of appearance for each docking conformation in five independent runs.

Figure 9 .
Figure 9.The alignment of the docked ligand (Yellow) with the initial ligand (Grey) illustrates a noteworthy similarity, affirming the reliability of the molecular docking protocol.

Figure 9 .
Figure 9.The alignment of the docked ligand (Yellow) with the initial ligand (Grey) illustrates a noteworthy similarity, affirming the reliability of the molecular docking protocol.

Table 2 .
The significance of the molecular descriptors constructed the developed model.

Table 3 .
The 2D visualization of the new designed compounds using the developed model.

Table 3 .
The 2D visualization of the new designed compounds using the developed model.

Table 3 .
The 2D visualization of the new designed compounds using the developed model.

Table 3 .
The 2D visualization of the new designed compounds using the developed model.

Table 3 .
The 2D visualization of the new designed compounds using the developed model.

Table 3 .
The 2D visualization of the new designed compounds using the developed model.

Table 3 .
The 2D visualization of the new designed compounds using the developed model.

Table 3 .
The 2D visualization of the new designed compounds using the developed model.

Table 4 .
The values of the descriptors constructing the developed model for the synthetized compounds, designed compounds with their calculated pIC 50 , and leverages.

Table 5 .
The created interactions of obtained complexes with binding scores, participated residues, molecular interactions, and distances expressed in Å.

Table 6 .
The predicted ADMET properties of analyzed compounds by using pkCSM online tools.